Assessing environmental costs and impacts of forestry activities: A multi-method approach to environmental accounting

Assessing environmental costs and impacts of forestry activities: A multi-method approach to environmental accounting

G Model ECOMOD-6810; No. of Pages 11 ARTICLE IN PRESS Ecological Modelling xxx (2013) xxx–xxx Contents lists available at SciVerse ScienceDirect Ec...

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G Model ECOMOD-6810; No. of Pages 11

ARTICLE IN PRESS Ecological Modelling xxx (2013) xxx–xxx

Contents lists available at SciVerse ScienceDirect

Ecological Modelling journal homepage: www.elsevier.com/locate/ecolmodel

Assessing environmental costs and impacts of forestry activities: A multi-method approach to environmental accounting Elvira Buonocore a , Tiina Häyhä a , Alessandro Paletto b , Pier Paolo Franzese a,∗ a b

Laboratory of Ecodynamics and Sustainable Development, Department of Environmental Sciences, Parthenope University of Naples, Centro Direzionale, Isola C4, 80143 Naples, Italy Agricultural Research Council, Forest Monitoring and Planning Research Unit (CRA-MPF), Villazzano, Trento, Italy

a r t i c l e

i n f o

Article history: Received 18 September 2012 Received in revised form 4 February 2013 Accepted 6 February 2013 Available online xxx Keywords: Forestry Timber Wood chips Province of Trento Environmental accounting Life cycle assessment

a b s t r a c t Concerns about greenhouse gas emissions and a possible future shortage of fossil resources are leading to a growing demand for wood biomass as a renewable material and energy source. In this context, forestry activities are increasing to meet the larger demand for wood biomass supply. Hence, there is also an increasing need for assessing environmental costs and impacts of forestry operations, considering both direct and indirect inputs supporting wood production systems as well as main outputs, co-products, and by-products. In this study, a multi-method assessment encompassing material, energy, and emergy demand as well as relevant emissions was implemented to explore the environmental performance and sustainability of timber and wood biomass production in the Alpine context of Fiemme and Fassa Valleys, Province of Trento (Italy). The Energy Return On Investment (EROI) calculated for timber and wood chips production was 51.9 and 28.1. These output/input energy ratios showed that the products’ energy content was high compared to the direct and indirect fossil energy invested in both production processes. The global to local ratio of abiotic material calculated for timber and wood chips was 3.58 and 2.95, proving that about 2 times more matter flows were extracted and processed elsewhere than locally to supply the production processes. The fraction of renewable emergy calculated for timber and wood chips was 81% and 75% while the Emergy Yield Ratio (EYR) was 4.57 and 3.86, respectively, proving that the forestry system is considerably supported by renewable and locally available resources. The release of CO2 was 855 and 133 t CO2 /yr when considering the whole Fiemme and Fassa Valleys. These last figures, compared to the potential of the two valleys for greenhouse gas mitigation, showed the ability of the investigated forestry sector to perform within the limits of the local carrying capacity in terms of CO2 emissions. Finally, the scenario analysis highlighted the maximum production level compatible with a sustainable forest exploitation. In conclusion, the development of a multi-method approach to environmental accounting allowed a comprehensive assessment of forestry operations, providing a tool useful for local managers and policy makers committed to implement an environmentally sound management of forestry activities. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Concerns about greenhouse gas emissions, rising energy prices and shortage of natural resources have led to an increasing interest for wood biomass as a renewable material and energy source. One target of the European Union is to reduce greenhouse gas emissions and use of fossil fuels by increasing up to 20% the share of renewable energy by 2020. Forests in Europe are growing at a high rate as only two-third of their annual increment of wood biomass is removed by felling. Still, Europe remains one of the main roundwood producers in the world and, therefore, the proportion of increment that is utilized

∗ Corresponding author. Tel.: +39 081 5476528; fax: +39 081 5476515. E-mail address: [email protected] (P.P. Franzese).

is likely to increase in the future (FAO, 2011; Köhl et al., 2011). The balance between net annual increment and annual felling has been the main criterion for assessing the sustainable exploitation of forest ecosystems over time. Many studies on forestry operations have focused the attention on greenhouse gas balance (Kilpeläinen et al., 2011; Lindner et al., 2002; Routa et al., 2011; Sonne, 2006; White et al., 2005). CO2 and other greenhouse gases are emitted as a consequence of harvesting, management, and logistic operations related to wood biomass production. The carbon neutrality of biomass production and use has been questioned due to indirect emissions of CO2 and other greenhouse gases (Pyörälä et al., 2012). Furthermore, direct inputs (inputs that are directly used and locally managed) and indirect inputs (energy and material flows used by upstream processes) are needed to support wood production systems. For this reason, it is important to account for both direct and indirect energy and

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Please cite this article in press as: Buonocore, E., et al., Assessing environmental costs and impacts of forestry activities: A multi-method approach to environmental accounting. Ecol. Model. (2013), http://dx.doi.org/10.1016/j.ecolmodel.2013.02.008

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material demand as well as output emissions due to forestry activities and management (Franzese et al., 2009; May et al., 2012). In previous studies, life cycle assessment (LCA) has been used to evaluate the environmental impact of forest ecosystem exploitation. Seppälä et al. (1998) carried out a LCA of the Finnish forest industry whereas Berg and Lindholm (2005) investigated energy use and environmental impacts of forest operations in Sweden. May et al. (2012) evaluated wood production from Australian softwood plantations compared to native hardwood forests, paying particular attention to embodied energy and water use. Franzese et al. (2009) compared the use of Gross Energy Requirement and Emergy Accounting to explore the sustainable production of short rotation willow production in Sweden. LCA has also been used to study the environmental sustainability of wood production and bioenergy chains in Italy. Valente et al. (2011) performed a LCA for evaluating the impacts of a wood biomass supply chain for heating plants in the Italian Alpine region. Cambria and Pierangeli (2012) evaluated the environmental impact of high quality timber production in southern Italy. Fantozzi and Buratti (2010) employed the LCA methodology to evaluate the efficiency and sustainability of wood pellet production in Italy by considering the whole process, from field growth to ash disposal. Similarly, Caserini et al. (2010) compared the environmental impact of wood biomass use in small domestic appliances and centralized combined heat and power plants in northern Italy, mainly focusing on net savings of greenhouse gas emissions when using wood biomass instead of fossil fuels. Other authors like Lindner et al. (2010) and Päivinen et al. (2012) assessed the sustainability of forestry-wood chains by implementing a multi-criteria assessment integrating environmental, economic, and social aspects. Evaluations based on only one criterion and disregarding the whole life cycle of forestry operations may lead to partial and often misleading results. Therefore, a life cycle assessment perspective and multi-criteria assessment frameworks should be widely applied to reach a more comprehensive understanding of human–environment interactions through a large set of indicators (Buonocore et al., 2012; Franzese et al., 2008, 2009, 2013; Häyhä et al., 2011; Ulgiati et al., 2006, 2010, 2011; Viglia et al., 2011). In this study, an extended LCA perspective was applied by integrating different environmental accounting methods within a consistent assessment framework capable of evaluating direct and indirect inputs of matter and energy as well as emissions and related impacts due to forestry operations. The multi-method approach was implemented to investigate the environmental performance and sustainability of forestry activities in Fiemme and Fassa Valleys (Province of Trento, North Italy), by focusing on the environmental costs and impacts of two integrated forestry operations: timber and wood chips production.

protection), and cultural ecosystem services (education, recreation). Fiemme and Fassa Valleys are located in the northeastern part of the Province of Trento and embrace a productive forest area of 40,000 ha. Norway spruce (Picea abies L.) is the dominant species mixed with larch (Larix europaea Mill.) and Scots pine (Pinus sylvestris L.). European larch and Scots pine are two pioneer tree species and, consequently, these species are abundantly present on abandoned rangelands and on steep slopes. The Swiss stone pine (Pinus cembra L.) is found at the higher elevations up to the timberline. Silver fir (Abies alba Mill.) and beech (Fagus sylvatica L.) are quite rare as their presence is restrained by the continental climate and human management, which always favored spruce because of its most valuable timber (Pollini et al., 1998; Price and Butt, 2000). In the Province of Trento all forestry activities take place according to forest management plans which are key planning documents for ensuring the sustainable management of local forest ecosystems. The forest management plans allow maintaining and fostering the implementation of multiple long-term sustainability goals (Carbone and Savelli, 2009). In particular, the forest management plans, regulating forest resource use over a medium term of 10 yrs, are drawn by forestry experts and approved by a local administrative office in charge for governing landscape and environmental resources of the Province of Trento. Forest management plans give indications regarding all actions needed to improve forest structure and growth, and suggest selective cutting practices allowing the remaining forest to naturally regenerate over time. The total forest area is divided into forest blocks representing homogenous forest areas. The balance between increment of wood biomass and felling ensures the sustainability of timber production over time. In the study area, the rate of wood production is relatively constant over time. The average annual increment for Fiemme and Fassa Valleys is 4.5 m3 ha−1 while the average annual felling is 2.8 m3 ha−1 . The average amounts of timber and wood chips produced in Fiemme and Fassa Valleys are about 106,000 and 9000 m3 /yr (PAT, 2010). In the Province of Trento, herbicides and fertilizers are not used in the forest management practices. Wood is annually assigned to forestry companies for cutting, preparation, yarding and transport. Logging operations start at the forest stand where trees are felled with chainsaws. The whole trees can be extracted with mobile cable yarders to a landing area where tree branches are removed and the stem is cut into appropriate lengths. Trees can also be felled, delimbed and bucked with chainsaws at the stump and then tree lengths are extracted by cable yarder or winches. By considering both forestry practices, more than half (57%) of the total amount of branches, bark and tree tops produced in the valleys is converted into wood chips for local bioenergy production while the remaining fraction (43%) is left on the soil for nutrient cycling. 2.2. The multi-method assessment framework

2. Materials and methods 2.1. The study area The Province of Trento is an Alpine region located in northeastern Italy. It covers a surface of about 620,700 ha in which more than half of the land area (345,180 ha) is covered with forest. Therefore, forest ecosystems play an important role in this region in generating provisioning ecosystem services such as timber, food (berries, mushrooms, and game), fodder, and wood biomass for energy purpose (Goio et al., 2008). In addition, other important ecosystem services provided by the forest ecosystem are: supporting (primary production, soil formation, nutrient cycling), regulating (climate regulation, water purification, hydro-geological

In this study, timber and wood chips production in Fiemme and Fassa Valleys was investigated by implementing a multi-method assessment framework including several environmental accounting methods jointly applied to provide a comprehensive set of intensive and extensive indicators referring to multiple scales and dimensions (Ulgiati et al., 2011). Extensive indicators account for total (direct and indirect) flows of environmental resources supporting the investigated system, also including hidden flows occurring at larger spatial and time scales. Extensive indicators are related to the physical size of the system while intensive indicators are relatively independent on the physical size of the system and provide a measure of environmental performance in relation to generated products (e.g., material or energy used per unit of generated products).

Please cite this article in press as: Buonocore, E., et al., Assessing environmental costs and impacts of forestry activities: A multi-method approach to environmental accounting. Ecol. Model. (2013), http://dx.doi.org/10.1016/j.ecolmodel.2013.02.008

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LCA studies are often more focused on matter and energy flows occurring under human control, while flows outside the market dynamics and flows which are not associated to significant matter and energy carriers (such as labor) are generally disregarded. To better explore the performance and sustainability of a production process, such flows should be also evaluated. In this study, the Emergy Synthesis method was used to expand the perspective of LCA by accounting for free renewable inputs, different forms of energy, materials, human labor and economic services on a common basis (solar energy), offering larger potentiality to explore the sustainable interplay of environment and economy (Franzese et al., 2009). Raugei et al. (in press) discussed the potential added value and lingering obstacles of integrating emergy into LCA, by concluding that emergy arguably offers the added value of a comprehensive donor-side assessment capable of providing an estimate of the total environmental support to a process. An inventory of all the inputs to and outputs from the forestry system was carried out and used for calculating a large set of performance and sustainability indicators. Such a common inventory ensured the maximum consistency of input data and inherent assumptions. The raw amounts of input flows from the inventory phase were multiplied by suitable conversion coefficients specific of each evaluation method to take into account the direct and indirect consumption of material, energy, and environmental resources supporting the forestry sector. Similarly, input flows were multiplied by emission factors to account for the total amount of airborne, liquid, and solid emissions. Then, the contribution of the emissions to different impact categories was calculated. The calculation procedures are summarized by the following equations: C=



fi · ci

i = 1, . . . , n

(1)

where C, cumulative material, energy or environmental demand associated to the investigated process and to final generated products; fi , raw amount of the ith inflow expressed as matter amount or energy content; ci , material or energy or environmental demand per unit of ith input flow (from published literature or calculated in this study). cp =

C fp

(2)

where cp , material or energy or environmental cost per unit of generated product; C, cumulative material, energy or environmental demand associated to the investigated process and to final generated products; fp , total raw amount of generated product expressed as matter amount or energy content. In this study, the inputs to the forestry sector support timber production as well as the additional step of wood chips production. Therefore, to assign proper environmental costs and impacts to the step of wood chips production, the input flows supporting stump site operations, extraction and landing operations for timber production were also assigned to wood chips by using a massbased allocation procedure. A mass-based allocation was chosen as wood residues are converted into wood chips: a commercially useful product having a specific utility (to power local thermal plants) and market price. In general, other allocation procedures are also possible, for instance economic-based allocation and “avoiding allocation” (i.e., allocation of all inputs to the only commercially useful product). The variation of the calculated indicators according to different allocation procedures was also assessed in this study and it is shown in Section 3. The environmental accounting methods integrated in the assessment framework can be assigned to two broad categories: (1) upstream methods (Material Flow Accounting, Gross Energy

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Requirement, and Emergy Accounting) focusing on the cumulative amount of environmental resources used per unit of generated product, and (2) downstream methods (emissions and impact categories), more concerned with the consequences of the system’s emissions. 2.2.1. The Material Flow Accounting (MFA) The Material Flow Accounting method (Schmidt-Bleek, 1993; Hinterberger and Stiller, 1998) aims at evaluating the environmental disturbance associated with the withdrawal or diversion of material flows from their natural ecosystemic pathways. This method estimates the amounts of input resources generating an environmental impact while supporting the production of a product or service. The material inputs accounted for are generally divided into four different input categories: (1) abiotic raw materials, (2) biotic raw materials, (3) water, and (4) air. Appropriate material intensity factors (MIFs), expressed as gram per unit of input, were multiplied by each input to the process, accounting for the cumulative amount of matter directly and indirectly required to provide those inputs to the system. MIFs were selected from the last updated MFA database of the Wuppertal Institute (2011) providing abiotic, biotic, water, air, and soil material intensity indicators. The resulting cumulative material flows aggregated for each environmental compartment (biotic and abiotic matter, water, air) were then assigned to the system’s outputs defining a quantitative measure of their cumulative environmental burden on that compartment. Material extractions cause changes in natural material flows and cycles, altering their natural pathways (Ritthoff et al., 2002). The MFA method helps to highlight the potential for process management aimed at reducing resource consumption on the local scale as well as on regional, national, and global scales. By expanding the scale of investigation, it becomes apparent how matter flows supplied to a process are often extracted and processed elsewhere. To make available each input to the process, matter flows are moved from place to place, processed, and finally disposed of. By evaluating the potential for environmental upstream impacts, the MFA method represents a useful tool for precautionary environmental protection. According to this methodology, the fewer raw materials are used the less environmental impact occurs. 2.2.2. The Gross Energy Requirement (GER) According to the International Federation of Institutes for Advanced Study (IFIAS, 1974), energy analysis has been defined as the process of determining the energy required directly and indirectly to allow a system to produce a good or service. The IFIAS conventions were mainly aimed at quantifying the availability and use of fossil fuels stocks (sometimes also referred to as “commercial energy”). In this framework, the Gross Energy Requirement (GER) method accounts for the amount of fossil energy that is required directly and indirectly by the process of making a good or service. More specifically, it focuses on fuels and electricity, fertilizers and other chemicals, machinery, and assets supplied to a process in terms of the direct and indirect fossil energy required to produce and make them available to the process (Slesser, 1978; Smil, 1991). The GER is expressed in energy units per physical unit of good or service produced (for instance, MJ per kg of steel). As the GER of a product is concerned with the depletion of fossil energy, all process inputs which do not require the use of direct and indirect fossil energy are not accounted for. Resources provided for free by the environment, for instance topsoil and spring water, are not accounted for by the GER method. Human labor and economic services are also not included in most evaluations (Franzese et al., 2009). Other energy analysis methods, like the Cumulative Energy Demand (CED), account for both non-renewable and renewable inputs. In this study, the GER method was chosen to specifically

Please cite this article in press as: Buonocore, E., et al., Assessing environmental costs and impacts of forestry activities: A multi-method approach to environmental accounting. Ecol. Model. (2013), http://dx.doi.org/10.1016/j.ecolmodel.2013.02.008

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address the consumption of direct and indirect fossil energy. Especially in the case of biomass and bioenergy, claimed to be “green” but often produced using considerable amounts of fossil energy, it is important to assess the total (direct and indirect) amount of fossil energy supporting the production process. Indeed, this is the amount of energy that would be necessary to replace in case of fossil energy shortage to continue producing that biomass or bioenergy. The GER of the ith input to the investigated process was calculated by multiplying the raw amount of that input by its energy intensity factor. Then, the total GER cost of the whole process was calculated as the sum of the Gross Energy Requirement of all inputs. Finally, the GER of the product was expressed as the total amount of fossil energy required per unit of generated output. Quantifying the total energy invested into a process allows an estimate of the total amount of primary energy used and, as a consequence, the extent of the depletion of non-renewable energy resources caused by the process. According to Cleveland (2011), the Energy Return On Investment (EROI) is defined as the ratio of the energy delivered by a process to the energy used directly and indirectly in that process. Raugei et al. (2012) defined the EROI for a fossil fuel as the ratio of the energy in a given amount of the extracted and delivered fuel to the total primary energy used in the supply chain. In recent studies, the energy investment related to societal-driven input flows (i.e., labor, services) has also been included by using a money-to-energy conversion factor. The lack of a univocal procedure for the calculation of the EROI has been recently discussed by Brown et al. (2012) and Raugei et al. (2012). In this case study, since most of the energy invested is of fossil origin, the EROI of timber and wood chips was calculated by dividing the energy content of the products by the total (direct + indirect) amount of fossil energy invested in input to the process. In particular, the EROI of timber was calculated as the ratio between the energy content of timber and the fraction of the fossil energy allocated to timber. Similarly, the EROI of wood chips was calculated as the ratio between the energy content of wood chips and the fraction of fossil energy allocated to residues plus the fossil energy required for wood chips production. 2.2.3. The Emergy Accounting The Emergy Accounting (Odum, 1988, 1996; Brown and Ulgiati, 2004) is an energy evaluation method rooted in irreversible thermodynamics and systems thinking. The method is aimed at evaluating the environmental performance of the system on the global scale of biosphere, also taking into account free environmental inputs (e.g., solar radiation, wind, rain, and geothermal flows) as well as indirect environmental support embodied in human labor and services (Franzese et al., 2009). Emergy Accounting is a measure of the cumulative environmental support to a process, and it allows exploring the interplay of natural ecosystem and human activities. According to this method, all inputs were accounted for in terms of their solar emergy, defined as the total amount of solar available energy (exergy) directly or indirectly required to make a given product or support a given flow, and measured as solar equivalent joules (seJ). The amount of emergy that is required to generate one unit of each input is referred to as its specific emergy in the case of mass flows (seJ/g) and solar transformity in the case of energy flows (seJ/J). Emergy intensity factors can be considered “quality” factors accounting for the environmental support provided by the biosphere to the formation of each input. Raw data on mass, energy, labor, and money flows were converted into emergy units and then summed into a total amount of emergy used by the system. The two products were first evaluated in units of mass or energy. Then, the specific emergy or the

solar transformity of the system’s products was calculated as the ratio between the total emergy used up by the process and the system’s output expressed in terms of mass amount or exergy content (Odum, 1996). Timber and wood chips are flows sharing the same physical–chemical characteristics and therefore, according to emergy algebra, they were treated as splits and consequently a mass-based allocation was used. In addition to the total emergy input (U) and the solar transformity, the main emergy-based indicators used to describe the environmental performance and sustainability of the investigated forestry system were: the Environmental Loading Ratio (ELR), the Emergy Yield Ratio (EYR), and the Emergy Sustainability Index (ESI) (Brown and Ulgiati, 2004). The ELR compares the amount of non-renewable (N) and purchased emergy (F) to the amount of locally available renewable emergy (R). In the case of a natural ecosystem, there would be no investments from outside and the system would be driven only by local renewable resources, thus having an ELR = 0. The EYR is a measure of the ability of a process to exploit and make available local resources by investing outside resources. The lowest possible value of the EYR is one, situation in which the emergy converging to generate the yield does not differ significantly from the emergy invested from outside the system. Finally, the ESI is an aggregated indicators calculated by the ratio of the EYR (sensitive to the outside-versus-local emergy use) and the ELR (sensitive to the nonrenewable-versus-renewable emergy use).

2.2.4. Emissions and impact categories Downstream impacts are associated with airborne and waterborne emissions and solid wastes. Each step of a process contributes to these three broad categories of emissions and each kind of emission is likely to generate an impact on the surrounding environment, be it humans, other species, or landscape and built environment. The assessment of these impacts can be performed in two different ways: (1) mid-point assessment, calculating the amount of emissions and assigning them to specific impact categories (i.e., damage potential), under the assumption that “less” is better; (2) end-point assessment, quantifying the extent to which a damage actually or potentially occurs (e.g., fraction of affected species; overall disease or disability generated, expressed as the number of years lost due to ill-health, disability or early death). The first choice is more easily quantifiable and less uncertain, while the second is much more affected by assumptions and side events. In this study, we used the CML2 baseline 2000 method (http://www.leidenuniv.nl) aimed at evaluating the potential environmental damage of airborne, liquid, and solid emissions by appropriate equivalence factors to selected reference compounds for each impact category. The potential impact of the investigated process for each category is calculated by multiplying all the emissions by their respective impact equivalence factors. The impact categories selected in this study were: (a) global warming potential (carbon footprint), expressed in grams of CO2 equivalent per gram of product, (b) acidification potential, expressed in grams of SO2 equivalent per gram of product, (c) eutrophication potential, expressed in grams of PO4 −3 equivalent per gram of product, (d) tropospheric ozone and photosmog formation potential, expressed in grams of C2 H4 equivalent per gram of product, (e) stratospheric ozone depletion potential, expressed in grams of CFC-11 equivalent per gram of product, (f) ecotoxicity potential, expressed in grams of 1,4-dichlorobenzene equivalent per gram of product.

Please cite this article in press as: Buonocore, E., et al., Assessing environmental costs and impacts of forestry activities: A multi-method approach to environmental accounting. Ecol. Model. (2013), http://dx.doi.org/10.1016/j.ecolmodel.2013.02.008

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Fig. 1. Systems diagram of forestry activities in Fiemme and Fassa Valleys (Province of Trento, Italy).

3. Results 3.1. Mass and energy flows in the forestry system A systems diagram was drawn according to a standardized energy systems language (Odum, 1994; Odum and Peterson, 1996; Odum and Odum, 2000) to model forestry operations in the study area, identifying the system’s boundary, main driving forces, generated outputs, and interactions among system’s components (Fig. 1). The renewable flows (solar radiation, wind, rain, geothermal flow), shown on the left side of the diagram, directly support the forest ecosystem, also providing indirect support to human activities. In addition to renewable flows, human-driven flows (fossil fuels, machinery, labor) imported from the human economy and supporting the forestry operations are shown as inflowing from the top of the diagram. The final products (timber and wood chips) are represented as outflowing from the right side of the diagram. Money flows, drawn by means of dotted lines in Fig. 1, enter from the right side of the systems diagram and flow out as payments for services associated to imports. It is important to note that money paid for imported resources only refers to the services associated to such resources. Services measure the indirect labor invested outside of the investigated system to extract and process the raw materials and make processed resources available to the production process (i.e., money is not paid to nature for its free resources but it is always paid to support direct and indirect labor). Flows accounted for in Table 1 were derived from the systems diagram, thus complementing the qualitative model of mass and energy flows with a quantitative environmental accounting. Table 1

Table 1 Main outputs and input flows supporting the forest ecosystem and forestry operations in Fiemme and Fassa Valleys (Province of Trento, Italy).

Input Forest ecosystem Local renewable resources Solar radiation Wind Rain Geothermal flow Imported resources Labor and services

Amount

Unit

2.07E+19 3.03E+14 8.92E+14 5.08E+14

J/yr J/yr J/yr J/yr

1.00E+05

D /yr

Timber production Imported resources Machinery (steel) Machinery (plastic) Fuel Labor and services

1.06E+08 1.18E+07 1.41E+08 2.65E+06

g/yr g/yr g/yr D /yr

Wood chips production Imported resources Machinery (steel) Machinery (plastic) Fuel Labor and services

2.38E+06 2.64E+05 1.27E+07 9.09E+04

g/yr g/yr g/yr D /yr

Output Standing wood biomass Timber Wood chips

1.63E+05 1.06E+05 9.03E+03

m3 /yr m3 /yr m3 /yr

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Table 2 MFA indicators calculated for timber and wood chips production in Fiemme and Fassa Valleys (Province of Trento, Italy). Material Flow Accounting Timber production Intensive indicators Abiotic material intensity per gram of timber Water demand per gram of timber Total material intensity per gram of timber Global to local ratio of abiotic material Extensive indicators Total abiotic material requirement Total water demand Wood chips production Intensive indicators Abiotic material intensity per gram of wood chips Water demand per gram of wood chips Total material intensity per gram of wood chips Global to local ratio of abiotic material Extensive indicators Total abiotic material requirement Total water demand

Value

Unit

0.02 0.08 0.005 3.58

g/g g/g g/g

8.12E+08 3.85E+09

g/yr g/yr

0.03 0.13 0.15 2.95

g/g g/g g/g

1.04E+08 5.24E+08

g/yr g/yr

summarizes the main input flows supporting the investigated forest ecosystem and forestry operations as well as the main generated outputs (standing wood biomass, timber, and wood chips). 3.2. Material Flow Accounting indicators Table 2 summarizes the MFA indicators calculated for timber and wood chips production in the study area. The abiotic material intensity of timber and wood chips was 0.02 g/gtimber and 0.03 g/gwood chips , respectively. These results show the amount of abiotic matter (minerals, soil, fuel, etc.) degraded or diverted to provide 1 g of the final products. The water demand per gram of timber was 0.08 g/gtimber , accounting for 80 L of water per ton of timber. The same indicator calculated per gram of wood chips was 0.13 g/gwood chips , accounting for 130 L of water per ton of wood chips. These results highlight the indirect water consumption associated with fuel refining and machinery manufacture. The total material intensity of timber and wood chips was 0.005 g/gtimber and 0.15 g/gwood chips (Table 2), showing a slight difference due to the additional water and abiotic material requirements for chipping operation. The global to local ratio of abiotic material for timber and wood chips production was 3.58 and 2.95 (Table 2). The total abiotic material requirement (calculated for the whole forestry system) for timber and wood chips was 8.12 × 108 and 1.04 × 108 g/yr while the total water demand was 3.85 × 109 and 5.24 × 108 g/yr, respectively.

Table 3 GER indicators calculated for timber and wood chips production in Fiemme and Fassa Valleys (Province of Trento, Italy). Gross Energy Requirement Timber production Intensive indicators Oil equivalent intensity per gram of timber Oil equivalent intensity per joule of energy content Fossil energy per gram of timber Embodied energy per joule of energy content EROI Global to local energy ratio Extensive indicators Total GER cost Total oil equivalent applied Wood chips production Intensive indicators Oil equivalent intensity per gram of wood chips Oil equivalent intensity per joule of energy content Fossil energy per gram of wood chips Embodied energy per joule of energy content Global to local energy ratio EROI Extensive indicators Total GER cost Total Oil equivalent applied

Value

Unit

0.006

goil /gtimber

4.6E−07

goil /J

239.05 0.02

J/gtimber J/J

51.9 2.1 1.14E+13 2.72E+08

J/yr goil /yr

0.01

goil /gwood chips

8.5E−07

goil /J

441.79 0.04

J/gwood chips J/J

1.86 28.1 1.80E+12 4.29E+07

J/yr goil /yr

Note: 1 g of oil = 41,860 J.

Fig. 2 shows the contribution of different input flows to the GER of timber and wood chips production. The consumption of diesel was dominant resulting in 56% of the total GER for timber and 64% of the total GER for wood chips. 3.4. Emergy Accounting indicators Emergy-based indicators calculated for the investigated forest ecosystem and its standing wood biomass as well as for timber and wood chips production are summarized in Table 4. Results show an increasing trend of the solar transformity (emergy invested per unit product) at each step of forestry: from standing wood biomass (3.01 × 104 seJ/J), to timber (3.70 × 104 seJ/J), to wood chips (4.11 × 104 seJ/J), pointing out the higher convergence of environmental support through the forestry production chain. Similarly, a decreasing trend of the renewability fraction (99%, 81%, and 75%) was obtained for the three steps of the forestry chain (Table 4). The ELR for standing wood biomass, timber and wood

3.3. Gross Energy Requirement indicators GER indicators are summarized in Table 3. The fossil energy per gram of timber and wood chips was 239.05 J/gtimber and 441.79 J/gwood chips while the total GER cost was 1.14 × 1013 and 1.80 × 1012 J/yr, respectively. These results reflect both the direct energy consumption (fuel) used for harvesting, extraction, landing operations and chipping, and the indirect energy invested to make available machinery and fossil fuels to the forestry system. The EROI of timber and wood chips was 51.9 and 28.1 (Table 3). Moreover, the global to local energy ratio for timber production was 2.1, meaning that about 45% of the total fossil energy consumption was direct while 55% was indirect. In the case of wood chips, the global to local energy ratio was 1.86, pointing out a larger share of direct fossil fuels consumption.

Fig. 2. Contributions of input flows to the GER of timber and wood chips production.

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Table 4 Emergy-based indicators calculated for the forest ecosystem, timber and wood chips production in Fiemme and Fassa Valleys (Province of Trento, Italy). Emergy indicators Forest ecosystem Intensive indicators Solar transformity of wood (with services) Solar transformity of wood (without services) Extensive indicators Emergy from local renewable resources, R Emergy from local non-renewable resources, N Emergy from imported resources, F Total emergy, U = R + N + F Renewable fraction Environmental Loading Ratio, ELR = (F + N)/R Emergy Yield Ratio, EYR = (R + N + F)/F Emergy Sustainability Index, ESI = EYR/ELR Timber production Intensive indicators Solar transformity of timber (with services) Solar transformity of timber (without services) Extensive indicators Emergy from local renewable resources, R Emergy from local non-renewable resources, N Emergy from imported resources, F Total emergy, U = R + N + F Renewable fraction Environmental Loading Ratio, ELR = (F + N)/R Emergy Yield Ratio, EYR = (R + N + F)/F Emergy Sustainability Index, ESI = EYR/ELR Wood chips production Intensive indicators Solar transformity of wood chips (with services) Solar transformity of wood chips (without services) Extensive indicators Emergy from local renewable resources, R Emergy from local non-renewable resources, N Emergy from imported resources, F Total Emergy, U = R + N + F Renewable fraction Environmental Loading Ratio, ELR = (F + N)/R Emergy Yield Ratio, EYR = (R + N + F)/F Emergy Sustainability Index, ESI = EYR/ELR

Value

3.01E+04 2.98E+04 2.72E+19 0.00E+00 2.46E+17 2.75E+19 99% 0.01 111.52 12,325

3.70E+04 3.03E+04 2.72E+19 0.00E+00 6.86E+18 3.14E+19 81% 0.25 4.57 18.11

4.11E+04 3.11E+04 2.18E+18 0.00E+00 7.68E+17 2.97E+18 75% 0.35 3.86 10.94

Unit

seJ/J seJ/J seJ/yr seJ/yr seJ/yr seJ/yr

seJ/J seJ/J seJ/yr seJ/yr seJ/yr seJ/yr

7

Table 5 Emissions calculated for timber and wood chips production in Fiemme and Fassa Valleys (Province of Trento, Italy). Emissions

Value

Unit

Timber production CO2 released CO2 per gram of timber CO2 per joule of energy content Global to local CO2 ratio CO released NOx released SO2 released Global unburnt hydrocarbon released NO2 released CH4 released

8.55E+08 0.02 1.45E−06 1.02 7.98E+05 1.92E+06 9.90E+05 1.04E+05 1.85E+04 2.06E+04

g CO2 /yr g CO2 /g g CO2 /J

Wood chips production CO2 released CO2 per gram of wood chips CO2 per joule of energy content Global to local CO2 ratio CO released NOx released SO2 released Global unburnt hydrocarbon released NO2 released CH4 released

1.33E+08 0.03 2.64E−06 2.67 1.49E+05 3.52E+05 1.29E+05 1.78E+04 3.46E+03 2.69E+03

g CO2 /yr g CO2 /g g CO2 /J

g CO/yr g NOx/yr g SO2 /yr g part./yr g NO2 /yr g CH4 /yr

g CO/yr g NOx /yr g SO2 /yr g part./yr g NO2 /yr g CH4 /yr

(R) accounted for 99% for standing tree biomass, 80% for timber and 73% for wood chips production (Fig. 3). seJ/J seJ/J seJ/yr seJ/yr seJ/yr seJ/yr

chips was 0.01, 0.25 and 0.35, respectively, while the EYR was 111.52, 4.57, and 3.86 (Table 4). In Fig. 3 the contribution of different emergy input flows supporting the natural growth of wood biomass and the production of timber and wood chips is shown. The flow of renewable emergy

3.5. Emissions and environmental impact categories Emissions calculated for timber and wood chips production (Table 5) are related to both the direct use of fuel in forestry activities and the indirect fuel consumption for fuel refining, extraction of metals, and machinery manufacture. The total CO2 released for timber and wood chips production was 8.55 × 108 and 1.33 × 108 g/yr (Table 5). Due to negligible soil erosion in the study area, these results did not include CO2 emissions generated by the loss of soil carbon. CO2 and other emissions contribute to a range of human health and environmental problems. Table 6 summarizes the contribution of the forestry activities performed in the study area to Global Warming Potential and other impact categories per functional unit (gram, joules, and economic value of product). These indicators show the performance of forestry activities in terms of potential environmental impact. 3.6. Comparison of performance indicators and scenario analysis To better compare the environmental performance of timber and wood chips production six intensive indicators are summarized in Table 7. Results are expressed per ton of generated products. Table 8 shows the variation of the same indicators calculated according to different allocation procedures: mass-based allocation, economic allocation, and “avoiding allocation”. In addition, to account for the environmental costs and impacts of the whole forestry system within the limits of the local carrying capacity in terms of maximum sustainable production, the actual state was compared with an alternative scenario characterized by a felling rate of 100% of the annual biomass increment (Table 9). Results are expressed as total figures at the scale of the whole Fiemme and Fassa Valleys per year. 4. Discussion

Fig. 3. Contribution of different emergy input flows supporting standing wood biomass, timber and wood chips production.

A multi-method approach to environmental accounting generating a set of performance and sustainability indicators was used to

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Table 6 Contribution to impact categories calculated for timber and wood chips production in Fiemme and Fassa Valleys (Province of Trento, Italy). Impact categories (CML 2 – baseline 2000)

Value

Unit

Timber production Global warming potential Global warming potential per gram of timber Global warming potential per unit of energy content Global warming potential per unit of economic value Human toxicity Human toxicity per gram of timber Human toxicity per unit of energy content Human toxicity per unit of economic value Photochemical oxidation Photochemical oxidation per gram of timber Photochemical oxidation per unit of energy content Photochemical oxidation per unit of economic value Acidification Acidification per gram of timber Acidification per unit of energy content Acidification per unit of economic value Eutrophication Eutrophication per gram of timber Eutrophication per unit of energy content Eutrophication per unit of economic value

8.61E+08 0.02 1.46E−06 83.64 2.40E+06 5.03E−05 4.06E-09 0.23 3.17E+05 6.64E−06 5.35E−10 0.03 2.15E+06 4.50E−05 3.63E−09 0.21 2.55E+05 5.34E−06 4.31E−10 0.02

g CO2 equiv. g CO2 equiv./g g CO2 equiv./J g CO2 equiv./D g 1.4-DB equiv. g 1.4-DB equiv./g g 1.4-DB equiv./J g 1.4-DB equiv./D g C2 H4 equiv. g C2 H4 equiv./g g C2 H4 equiv./J g C2 H4 equiv./D g SO2 equiv. g SO2 equiv./g g SO2 equiv./J g SO2 equiv./D g PO4 equiv. g PO4 equiv./g g PO4 equiv./J g PO4 equiv./D

Wood chips production Global warming potential Global warming potential per gram of wood chips Global warming potential per unit of energy content Global warming potential per unit of economic value Human toxicity Human toxicity per gram of wood chips Human toxicity per unit of energy content Human toxicity per unit of economic value Photochemical oxidation Photochemical oxidation per gram of wood chips Photochemical oxidation per unit of energy content Photochemical oxidation per unit of economic value Acidification Acidification per gram of wood chips Acidification per unit of energy content Acidification per unit of economic value Eutrophication Eutrophication per gram of wood chips Eutrophication per unit of energy content Eutrophication per unit of economic value

1.34E+08 0.03 2.66E−06 594.81 4.35E+05 1.07E−04 8.63E−09 1.93 5.62E+04 1.38E−05 1.12E−09 0.25 3.31E+05 8.14E−05 6.57E−09 1.47 4.67E+04 1.15E−05 9.27E−10 0.21

g CO2 equiv. g CO2 equiv./g g CO2 equiv./J g CO2 equiv./D g 1.4-DB equiv. g 1.4-DB equiv./g g 1.4-DB equiv./J g 1.4-DB equiv./D g C2 H4 equiv. g C2 H4 equiv./g g C2 H4 equiv./J g C2 H4 equiv./D g SO2 equiv. g SO2 equiv./g g SO2 equiv./J g SO2 equiv./D g PO4 equiv. g PO4 equiv./g g PO4 equiv./J g PO4 equiv./D

explore the investigated production processes under different perspectives: (a) direct and indirect fossil fuel consumption, (b) abiotic material demand, (c) water consumption, (d) generated emissions and related impact categories, and (e) the global environmental support (i.e., emergy). The main environmental costs and impacts of the forestry activities in the study area were due to the direct and indirect consumption of fossil fuels. For a better comparison, the environmental performance of the two forestry products was assessed by means of six selected indicators plotted through a radar graph (Fig. 4). All the intensive indicators calculated for wood chips production resulted higher than for timber (Table 7). The higher figures were due to the greater use of diesel and machinery related to the additional step of chipping that is an energy intensive operation. Still, the intensity factors of wood chips calculated in this study

showed figures much lower than those characterizing fossil fuels (Häyhä et al., 2011, Table 1), thus proving the desirability of wood chips utilization for bioenergy production in the study area. It should be also remarked that, by adopting an economic allocation or “avoiding allocation”, the indicators of wood chips show better figures compared to the results obtained by using a massbased allocation (Table 8). Still, since this study has a main focus

Table 7 Indicators of environmental performance for timber and wood production. Intensive indicator

Timber

Wood chips

Energy intensity (Joil /t) Solar transformity (seJ/J) Abiotic material intensity (g/t) Water demand (g/t) CO2 emissions (g CO2 /t) Global warming potential (g CO2 equiv./t)

2.39E+08 3.70E+04 1.70E+04 8.07E+04 1.79E+04 1.81E+04

4.42E+08 4.11E+04 2.55E+04 1.29E+05 3.28E+04 3.30E+04

Fig. 4. Comparison of environmental performance indicators for timber and wood chips production (indicators in Fig. 4 are normalized from Table 7).

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Table 8 Variation of selected indicators calculated by using mass-based allocation, economic allocation, and avoiding allocation. Indicator

Unit

Mass-based allocation

Economic allocation

Avoiding allocation

Timber production Abiotic material intensity per gram of timber Water demand per gram of timber Total abiotic material requirement Total water demand Fossil energy per gram of timber Total GER CO2 released Global warming potential

g/g g/g g/yr g/yr J/g J/yr g CO2 /yr g CO2 equiv.

0.017 0.08 8.12E+08 3.85E+09 239.05 1.14E+13 8.55E+08 8.61E+08

0.018 0.09 8.65E+08 4.10E+09 254.64 1.22E+13 9.11E+08 9.18E+08

0.018 0.09 8.82E+08 4.18E+09 259.84 1.24E+13 9.30E+08 9.36E+08

Wood chips production Abiotic material intensity per gram of wood chips Water demand per gram of wood chips Total abiotic material requirement Total water demand Fossil energy per gram of wood chips Total GER CO2 released Global warming potential

g/g g/g g/yr g/yr J/g J/yr g CO2 /yr g CO2 equiv.

0.03 0.13 1.04E+08 5.24E+08 441.79 1.80E+12 1.33E+08 1.34E+08

0.01 0.07 5.08E+07 2.73E+08 258.70 1.05E+12 7.74E+07 7.81E+07

0.01 0.05 3.31E+07 1.89E+08 197.67 8.03E+11 5.88E+07 5.94E+07

Table 9 Total environmental costs and impacts: comparison between the actual state and an alternative scenario defined by a maximum sustainable production level. Indicators

Actual timber

Maximum timber

Actual wood chips

Maximum wood chips

Total amount of forestry product (m3 /yr) Total emergy (seJ/yr) Total GER (J/yr) Total abiotic material requirement (g/yr) Total water demand (g/yr) CO2 released (g CO2 /yr) Global warming potential (g CO2 equiv.)

1.06E+05 3.14E+19 1.14E+13 8.12E+08 3.85E+09 8.55E+08 8.61E+08

1.63E+05 3.73E+19 1.48E+13 8.98E+08 4.47E+09 1.10E+09 1.11E+09

9.03E+03 2.97E+18 1.80E+12 1.04E+08 5.24E+08 1.33E+08 1.34E+08

2.12E+04 3.52E+18 3.01E+12 1.35E+08 7.44E+08 2.21E+08 2.23E+08

on a biophysical (i.e., matter and energy flows) perspective and due to the unstable and fast growing market price of wood chips, a mass-based allocation was chosen. The information provided by intensive indicators is useful to explore the performance of timber and wood chips production. However, such information should be analyzed together with the total environmental costs and impacts at regional level by taking into account the size of the two production processes. In fact, all the extensive indicators calculated at the level of the whole Fiemme and Fassa valleys resulted higher for timber due to its much larger production (Table 9 and Fig. 5). While local managers can be more interested in the characterization of the production process by focusing on the intensive indicators (to improve technological solutions), policy makers may

Fig. 5. Scenario analysis: comparison between actual and maximum production level (indicators normalized from Table 9).

be more interested in the figures showing the total environmental costs and impact at regional level (outcomes that can be used in support of environmental planning, scenario and trade-off analyses). The energy intensity (Joil /tproduct ) and CO2 emissions (gCO2 /tproduct ) of wood chips were about 80% higher than for timber (Table 7). As a consequence of the greater energy intensity, the EROI of wood chips was 46% smaller than for timber production. The solar transformity and the abiotic material intensity were 11% and 50% higher for wood chips than for timber (Table 7). The global to local ratio of abiotic material calculated for timber showed that about 2.5 times more matter flows were extracted and processed elsewhere than locally to supply the investigated production process, proving the need for assessing environmental costs and impacts at multiple scales. In the case of wood chips, the indirect consumption of abiotic matter was about 2 times higher (Table 2). The GER method showed the relevance of fossil resources for managing the investigated forestry system. The direct use of fossil fuels resulted about 50% of the total Gross Energy Requirement (Fig. 2). The application of the Emergy Accounting method provided a complementary perspective, highlighting the importance of free renewable resources supporting the growth of forest wood biomass on which the forestry operations are based (Fig. 3). In fact, while the GER method only accounted for fossil resources, the Emergy Accounting method was capable of considering also renewable energy flows, human labor, and economic services. The ELRs and EYRs calculated for standing wood biomass, timber, and wood chips production (Table 4) showed that the forestry system as a whole is mainly driven by locally available renewable resources and only moderately supported by non-renewable resources imported from outside the system. This is an important point when considering the sustainability of the forestry system since a sustainable system, by definition, should be largely run on local and renewable resources.

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From the viewpoint of the energy efficiency, the energy content of the generated products was high compared to the fossil energy used in support of the forestry operations. The high values of the EROI of timber (51.9) and wood chips (28.1) can be explained by the fact that a high energy return is provided by exploiting the work of nature (primary production) at a low commercial energy investment. A different picture is usually obtained in agricultural systems where the share of renewable resources is much lower due to a massive use of fossil resources in terms of direct consumption (fuels) and indirect consumption (chemicals and machinery). For instance, Franzese et al. (2009) calculated an EROI of 3.82 for corn production in Italy, a typical high chemical input crop production. Another important point in relation to the long-term sustainability of forestry operations is that the felling rate of timber should not exceed the natural rate of regeneration of wood biomass. In the case of the investigated forestry system, the felling rate was about 60% of the total annual increment, meaning that the stock of natural capital was not depleted over time. To explore the sustainable management of forestry activities within the limits of the local carrying capacity, the actual forestry production and related indicators calculated for the whole forestry system were compared to an alternative scenario characterized by a felling rate of 100% of the annual biomass increment (Fig. 5). The larger size of the area of the scenario with maximum timber and wood chips production shows a higher environmental cost and impact due to the larger production level. Contrary to the results of the intensive indicators in Fig. 4, the extensive indicators calculated for the whole Fiemme and Fassa Valleys show larger figures for timber than for wood chips production. This is because extensive indicators are dependent on the volume of production, which is much larger in the case of timber. In the case of the scenario with maximum production level, the total CO2 emissions released for timber and wood chips production and from the combustion of wood chips were about 15,000 t CO2 /yr. This value, compared with the potential of the two valleys for greenhouse gas mitigation accounting for a carbon sequestration of 227,000 t CO2 /yr (Rodeghiero et al., 2010), proved the ability of the investigated forestry system to perform within the limits of the local carrying capacity in terms of CO2 emissions. The scenario with maximum production level of timber and wood chips could entail a greater economic income but it should be considered that removing 100% of the annual increment of wood biomass (as timber and forestry residues converted to wood chips) from the forest ecosystem may have ecological implications affecting other ecosystem services, for instance in terms of nutrient cycling. In fact, since needles, twists, and branches are rich in nutrient content, removing wood biomass at a high rate to supply bioenergy plants can – in the long run – cause nutrient losses and reduce the growth of future tree generations, also if a possible compensation could be operated by returning wood-based ash to the forest as fertilizers (Pyörälä et al., 2012). A sustainable forest management should entail the perspective offered by the theoretical framework of Industrial Ecology (Korhonen et al., 2001) based on the maximization of waste matter and energy flow exchanges within a production pattern, like ecosystems do. The multi-method environmental accounting represents an effective tool to pursue the vision and objectives of Industrial Ecology as it is capable of providing a quantitative assessment of matter and energy flow exchanged in forestry and other human-managed production systems.

5. Conclusion An accurate forest management plan aimed at maximizing the environmental performance while minimizing the environmental

impact of forestry activities should take into consideration several different aspects, among which: fossil fuels and material flows consumption, renewable resources supply, generated impacts at local and global scale, and ecological constraints. This study showed that a multi-method approach to environmental accounting can be usefully applied to provide a comprehensive assessment of forestry activities and management. Such a multi-method approach can support local managers and policy makers committed to implement an environmentally sound management of forestry activities. The calculated indicators proved that the production of wood biomass for bioenergy purposes in the study area represents an interesting option as the process is highly supported by local and renewable resources, thus providing a viable alternative for replacing the use of fossil energy with wood biomass. In addition, in terms of CO2 emissions, the investigated processes showed their ability to perform within the limits of the local carrying capacity. Moreover, the calculated indicators provide a benchmark for future comparisons with similar systems and for monitoring the trend of the investigated forestry system over time. Future studies could be oriented to integrate the applied multi-method assessment framework with other methods capable of capturing economic and social aspects related to forest use and management, thus fully exploring constraints and potentialities of forest management across environmental, economic, and social dimensions. References Berg, S., Lindholm, E.L., 2005. Energy use and environmental impacts of forest operations in Sweden. Journal of Cleaner Production 13, 33–42. Brown, M.T., Ulgiati, S., 2004. Emergy analysis and environmental accounting. Encyclopedia of Energy 2, 329–354. Brown, M.T., Raugei, M., Ulgiati, S., 2012. On boundaries and ‘investments’ in emergy synthesis and LCA: a case study on thermal vs. photovoltaic electricity. Ecological Indicators 15, 227–235. Buonocore, E., Franzese, P.P., Ulgiati, S., 2012. Assessing the environmental performance and sustainability of bioenergy production in Sweden: a life cycle assessment perspective. Energy 37, 69–78. Cambria, D., Pierangeli, D., 2012. Application of a life cycle assessment to walnut tree (Juglans regia L.) high quality wood production: a case study in southern Italy. Journal of Cleaner Production 23, 37–46. Carbone, F., Savelli, S., 2009. Forestry programmes and the contribution of the forestry research community to the Italy experience. Forest Policy and Economics 11, 508–515. Caserini, S., Livio, S., Giugliano, M., Grosso, M., Rigamonti, L., 2010. LCA of domestic and centralized biomass combustion: the case of Lombardy (Italy). Biomass and Bioenergy 34, 474–482. Cleveland, C.J., 2011. Energy return on investment (EROI). In: Cleveland, C.J. (Ed.), Encyclopedia of Earth. Environmental Information Coalition, National Council for Science and the Environment, Washington, DC. Fantozzi, F., Buratti, C., 2010. Life cycle assessment of biomass chains: wood pellet from short rotation coppice using data measured on a real plant. Biomass and Bioenergy 34, 1796–1804. FAO, 2011. State of the World’s Forests 2011. Franzese, P.P., Russo, G.F., Ulgiati, S., 2008. Modeling the interplay of environment, economy and resources in marine protected areas. A case study in Southern Italy. Ecological Questions 10, 91–97. Franzese, P.P., Rydberg, T., Russo, G.F., Ulgiati, S., 2009. Sustainable biomass production: a comparison between gross energy requirement and emergy synthesis methods. Ecological Indicators 9, 959–970. Franzese, P.P., Cavalett, O., Häyhä, T., D’Angelo, S., 2013. Integrated Environmental Assessment of Agricultural and Farming Production Systems in the Toledo River Basin (Brazil). United Nations Educational, Scientific and Cultural Organization (UNESCO), 71 pp., ISBN: 978-92-3-001138-3. Goio, I., Gios, G., Pollini, C., 2008. The development of forest accounting in the province of Trento (Italy). Journal of Forest Economics 14, 177–196. Hinterberger, F., Stiller, H., 1998. Energy and material flows. In: Ulgiati, S., Brown, M.T., Giampietro, M., Herendeen, R.A., Mayumi, K. (Eds.), Advances in Energy Studies. Energy Flows in Ecology and Economy. Musis Publisher, Roma, Italy, pp. 275–286. Häyhä, T., Franzese, P.P., Ulgiati, S., 2011. Economic and environmental performance of electricity production in Finland: a multicriteria assessment framework. Ecological Modelling 223, 81–90. IFIAS, 1974. International Federation of Institutes for Advanced Study. In: Slesser, M. (Ed.), Energy Analysis Workshop on Methodology and Conventions. Report IFIAS No. 89, Stockholm. Kilpeläinen, A., Alam, A., Strandman, H., Kellomäki, S., 2011. Life cycle assessment (LCA) tool for estimating net CO2 exchange of forest production. GCB Bioenergy 3, 461–471.

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Please cite this article in press as: Buonocore, E., et al., Assessing environmental costs and impacts of forestry activities: A multi-method approach to environmental accounting. Ecol. Model. (2013), http://dx.doi.org/10.1016/j.ecolmodel.2013.02.008